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Article

Jun 18, 2024

14min read

How To Build A Customer Journey Map

Emily Healy

Understanding your customers’ paths is no easy task. Each user has their own unique reason for visiting your site and an individual route that they take as they explore your pages.

How can you gain insights about your customers to improve your website’s usability and understand buying trends?

The answer is simple: build a customer journey map.

In this blog, we’ll dive into a few things: what is a customer journey, a customer journey map, how to map the customer journey visually, templates of different customer journeys, a step by step guide for how to create them, and examples of customer journeys in action. Let’s get started

What is a customer journey?

A customer journey is a combination of all the interactions customers have with your brand before reaching a specific goal.

Creating a compelling journey helps you stand out and shows customers that you care about their experience. An enjoyable customer journey promotes positive engagement, making for more satisfied customers that are more likely to return for repeat purchases.

By better understanding your customers, you’ll be able to provide them with the best possible user experience every time they visit your online store. The best way to do this is by creating visual customer journey maps that present all this information about customers at a glance.

What is a customer journey map?

A customer journey map is a visual representation of a customer’s interaction with your business or website. It’s used to define which parts of this process might not be working as smoothly as they should be, thus improving the customer’s experience.

The customer journey map is a (mostly) visual tool that helps businesses understand what a customer goes through when buying a product or service from them. It maps out in clear, concise, visual terms, the journey each customer is likely to experience through buyer personas and user data.

The best customer journey map is a story, brought to life visually, of the customer’s experience. In essence, the best customer journey map is a story, brought to life visually, of the customer’s experience. It should be noted, however, that more complex information on the map may require text.

The map itself highlights “touchpoints, which are specific elements of the customer’s interaction with a business. Each of these touchpoints – for example, seeking a product, researching its content, buying the product, waiting for delivery, and returning it if unsatisfied – can be judged as negative, neutral, or positive from the customer’s perspective.

Customer journey maps require various research techniques that include hard data, customer feedback, and creative thinking. As such, no two maps are the same and each one will depend on many different factors that can’t be simplified or stereotyped as a matter of course.

The heart of customer journey maps: Buyer personas

Buyer personas are at the heart of a customer journey map tool and are broad representations, presented as fictional characters, based on real-life data and customer feedback. Typically, each project will create between three and seven buyer personas, each of which will require its own customer journey map.

The point of the customer journey map is to understand, as clearly as possible, what a customer will encounter when using your service. It will also help you improve the elements that are not functioning properly, are not easy to navigate, and show you how to make the entire experience more satisfying.

Each persona, and therefore the journey map itself, is not meant to be a perfect illustration of actual interactions. Rather, it’s a broad representation of the experience from the persona’s perspective.

Who Can Benefit From A Customer Journey Map?

There are many reasons why a customer journey map can be useful to a business. Customer satisfaction is more important than ever to a business, and it’s tied to loyalty to an extent that has not previously existed. Customers are more demanding, aware of their options, and willing to shop around.

By mapping each of the previously mentioned touchpoints, a well-designed customer journey map template can highlight any problems that clients might experience in the process of interacting with a business and help foster a relationship with an organization, product, service, or brand. This can occur across multiple channels and over a long period of time.

Once a customer journey map template has been designed, the entire enterprise can keep the customer at the forefront of the decision-making process. With a focus on the customer and their experience, or user experience (UX), any kinks, holes, or brick walls within the timeline’s touchpoints can be ironed out.

Bringing Together All Aspects Of The Business

Customer journey maps can help a business by bringing together departments with a focus on customer experience. To begin with, all departments can be engaged to discuss issues that customers may face when dealing with them. This is no small thing as many departments may not be used to dealing with customers, yet the decisions they take may have a profound effect on UX. By creating an understanding of how each touchpoint affects UX across the entire business, decisions can be made from an empathetic perspective.

Traditional marketing stops at the point of purchase, but customer experience does not necessarily end there. For example, perhaps the purchase was not to their satisfaction and they want to return the goods. Departments that might not typically be involved in touchpoints before purchase now have a central role to play. How easy is it for the customer to find the return information on a website? If they need information on delivery or collection times, how likely are they to get a response that will satisfy them? This all requires forethought and a policy that keeps customer experience central to design and organization.

How to map the customer journey visually

A customer journey map is a visual representation that helps you gain better insight into your customers’ experiences (from start to finish) from their point of view.

There are two vital elements to creating a customer journey map:

Defining your customers’ goals
Understanding how to map their nonlinear journey

By mapping out a customer’s digital journey, you are outlining every possible opportunity that you have to produce customer delight. You can then use these touchpoints to craft engagement strategies.

According to Aberdeen Group (via Internet Retailer), 89% of companies with multi-channel engagement strategies were able to retain their customers, compared to 33% of those who didn’t.

To visually map every point of interaction and follow your customer on their journey, you can use Excel sheets, infographics, illustrations, or diagrams to help you better understand.

Customer journey maps also help brands with:

Retargeting goals with an inbound viewpoint
Targeting a new customer group
Forming a customer-centric mindset

All of these lead to better customer experiences, which lead to more conversions and an increase in revenue.

Want more information on the digital customer journey? Check out our digital customer journey resource kit for a detailed e-book, an editable workbook, a use case booklet, and an infographic.

Examples of Customer Journey Map Templates and Which to Choose

There are four different types of customer journey maps to choose from. Each map type highlights different customer behaviors as they interact with your business at different points in time. Choosing the right template is essential based on your goals.

Current state template

The current state template is the most commonly used journey map that focuses on what customers currently do, their way of thinking, and how they feel during interactions.

It’s great for highlighting existing pain points and works best for implementing incremental changes to customer experiences.

2. Future state template

The future state template focuses on what customers will do, think, and feel during future encounters. It’s useful for conveying a picture of how customers will respond to new products, services, and experiences.

3. Day in the Life Template

This template is similar to the current state template because it visualizes present-day customer behaviors, thoughts, and feelings. However, this template assesses how customers behave both with your organization and with peers in your area.

This type of journey map works best for spurring new initiatives by examining unfulfilled needs in the market.

4. Service Blueprint Template

When creating a service blueprint template, you typically begin with an abridged version of a current or future state journey map. Then you add a network of people, methods, procedures, and technologies responsible for giving a simplified customer experience, either in the present or in the future.

Current state blueprint maps are beneficial for recognizing the source of current pain points, whereas future state blueprint maps help create an environment that will be necessary for providing a planned experience.

How to Create a Customer Journey Map (7 Steps)

Creating customer journey maps may feel repetitive, but the design and application you choose will vary from map to map. Remember: customer journeys are as unique as your individual customers.

Step 1: Create Buyer Personas

Before creating a journey map, it’s important to identify a clear objective so you know who you’re making the map for and why. Building personas is the most time-consuming part of the process. It requires detailed research, including qualitative and quantitative data, and is the foundation of the entire process. A persona is a highly relatable and rounded fictional character, generalized, but not stereotyped.

Buyer personas help define customer goals, providing a deeper understanding of their needs and topics of interest. More detail makes for more realistic personas, which means you’ll need to do a fair amount of market research to acquire this data.

Start by creating a rough outline of your buyer’s persona with demographics like age, gender, occupation, education, income, and geography. When you have that in place, you’ll need to get psychographic data on your customers. This kind of information may be harder to collect compared to demographic data, but it is worthwhile to understand customer preferences, needs and wants.

In short, demographics tell you who your customers are and psychographics provide insights into the why behind their behavior. Collecting concrete data on your customers helps you serve them better and deliver a more personalized user experience.

Collecting concrete data on your customers helps you serve them better and deliver a more personalized user experience.

Step 2: Select Your Target Customer

After making several customer personas, it’s time to do a “deep dive” into each to build a more accurate reflection of their experience.

Start by analyzing their first interaction with your brand and mapping out their movements from there.

What questions are they trying to answer? What is their biggest priority?

Step 3: List Customer Touchpoints

Any interaction or engagement between your brand and the customer is a touchpoint.

List all the touchpoints in the customer journey, considering everything from the website to social channels, paid advertisements, email marketing, third-party reviews, or mentions.

Which touchpoints have higher engagement? Which touchpoints need to be optimized?

All customer journey mapping examples are unique. Therefore, touchpoints on one map are unlikely to work for another. In fact, every business needs to update its buyer personas and customer journey maps as their business changes. Even quite subtle changes can have profound effects on the customer journey map template.

Step 4: Identify Customer Actions

Once you have identified all your customer touchpoints, identify common actions your customers make at each step. By dividing the journey into individual actions, it becomes easier for you to improve each micro-engagement and move them forward along the funnel.

Think of how many steps a customer needs to reach the end of their journey. Look for opportunities to reduce or streamline that number so customers can reach their goals sooner. One way to do this is by identifying obstacles or pain points in the process and creating solutions that remove them.

This is a great time to use the personas you created. Understanding the customer will help you troubleshoot problem areas.

Anticipating what your customer will do is another important part of mapping the customer journey. Accurate predictions lead to you providing better experiences, which ultimately leads to more conversions.

Step 5: Understand your available resources

Creating customer journey maps presents a picture of your entire business and highlights every resource being used to build the customer experience.

Use your plan to assess which touchpoints need more support, such as customer service. Determine whether these resources are enough to give the best customer experience possible. Additionally, you can correctly anticipate how existing or new resources will affect your sales and increase ROI.

Step 6: Analyzing the Customer Journey

An essential part of creating a customer journey map is analyzing the results.

Now you have your data, customer journey mapping template, touchpoints, and goals, it’s time to put it all together and define where the UX is meeting expectations and where things can be improved. It is important to note that mapping where things are going well is almost as important as defining what isn’t. Some elements of the journey can be spread to other areas.

As you assess the data, look for touchpoints that might drive customers to leave before making a purchase or areas where they may need more support. Analyzing your finished customer journey map should help you address places that aren’t meeting customers’ needs and find solutions for them.

Take the journey yourself and see if there’s something you missed or if there is still room for improvement. Doing so will provide a detailed view of the journey your customer will take.

Follow your map with each persona and examine their journeys through social media, email, and online browsing so you can get a better idea of how you can create a smoother, more value-filled experience.

One of the best ways of pinpointing where things are not going to plan is through customer feedback. This is typically done through surveys and customer support transcripts.

Step 7: Take Business Action

Having a visualization of what the journey looks like ensures that you continuously meet customer needs at every point while giving your business a clear direction for the changes they will respond to best.

Any variations you make from then on will promote a smoother journey since they will address customer pain points.

A great way to test your variations to find out what better serves your customers throughout their user journey is by leveraging A/B testing.

AB Tasty is a best-in-class A/B testing solution that helps you convert more customers by leveraging experimentation to create a richer digital experience – fast. This experience optimization platform embedded with AI and automation can help you achieve the perfect digital experience with ease.

Analyzing the data from your customer journey map will give you a better perspective on changes you should make to your site to reach your objective.

Once you implement your map, review and revise it regularly. This way, you will continue to streamline the journey. Use analytics and feedback from users to monitor obstacles.

Customer Journey Map Examples

Customer journey map templates are varied, some appear like works of art, while others are the work of a child, but as long as they are clear and concise, they can be effective.

This customer journey map for the charity ‘The Samaritans’ is a highly empathetic map, focused on the purpose of the charity itself. Note how the text is highly visual and therefore makes it easy to relate to the image of the map itself.

This is an example of a map that gives the impression of a journey, rather than a linear UX. This can help push home the point that customer experience is rarely easy to define as a journey from A to B.

The Truth about Customer Journeys

Customer journeys are ever-changing. Journey maps help businesses stay close to their customers and continuously address their needs and pain points. They provide a visual of different customers which helps to understand the nuances of their audience and stay customer-focused.

Customer journey maps can vary widely, but all maps share the same steps. With regular updates and the proactive removal of roadblocks, your brand can stand out, provide meaningful engagement, improve customer experiences, and see positive business growth.

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Jun 10, 2024

13min read

Customization vs Personalization: What’s the Difference?

AB Tasty

How do you grab a customer’s attention when our world has us on digital information overload?

Capturing attention and standing out in a market saturated with so many options is a challenge now more than ever. To overcome the digital noise, many businesses are aiming to enhance the customer experience with two approaches: customization vs personalization.

What’s the difference between customization and personalization? Are they two terms we can use interchangeably or are they two entirely different concepts?

You guessed it – customization and personalization describe two very different, but not entirely unrelated, practices. Though not the same, (mass) customization and personalization strategies revolve around people’s deep-seated desires for self-expression and recognition – as well as their limited attention spans in a loud, digital world.

Let’s take a deep dive together into the sea of information we have about customization vs personalization.

What’s the difference between customization and personalization?

Customization and personalization are often thought of as synonyms, but while some crossover certainly exists, they are very different terms referring to very different practices. They overlap in their ability to make the customer feel as if they have a unique relationship with the brand in a way that the mass marketing of the past could never achieve.

Both practices have proven particularly well suited to Millennials, the largest consumer age group, who have an instinctive mistrust of older forms of advertisement but are more influenced by both customization and personalization-based marketing. So what exactly are the differences and how do they work?

Customization Definition: Giving the power to the users

The most common form of customization comes in the form of product customization and is typically found online. Product customization is particularly popular for clothing outlets, whereby shoppers can design a piece of clothing from an online template, using different colors, fabrics, and shapes to make their own ‘unique’ product.

Customization examples:

Nike is one of the most important innovators in the field, allowing you to customize every aspect of your sneaker.

Another brand that has used product customization to great effect is Burberry, which has gone from strength to strength in recent years, partly due to being an innovator in the field of product customization.

This is sometimes referred to as ‘mass customization’ whereby online tools allow the customization process to produce bespoke products at mass-produced prices.

What is mass customization?

Mass customization is all about customer experience, sometimes referred to as CX, which marks an enormous shift from shopping habits that were once almost entirely about the product.

Interacting with brands and producing individual styles, or at least appearing to, is at least as important to Millennials as the functionality of a product, and there is little reason to think that subsequent generations will not have similar expectations in the future, especially with technology becoming so ubiquitous and powerful. Wherever retail goes in the near future, CX is going to be an essential part of it. While the cutting edge of customization is not a major preserve of older generations, the need to keep such platforms simple and easy to use should not be ignored.

One of the issues facing businesses offering product customization is how quickly can each purchase be produced? Convenience is also thought to be a major concern for shoppers in the 21st century, and patience is not seen as a virtue. This goes to the heart of the customer experience and will likely inform how successful the model is for each business. The more complex mass customization becomes, the larger on-demand mass production will be.

One thing is for sure, what was once the preserve of the burger joint or ice cream parlor, customization is fast spreading to all aspects of retail, and as a result, is changing everything.

Personalization Definition: Tailor your user’s entire experience

Personalization is one of the most misunderstood marketing terms of all.

Often misunderstood for customization, recommendation, and even optimization, it is instead a set of well-defined practices with an overall purpose or goal. Unlike customization, which offers a very specific set of tools for customer interaction, personalization has far-reaching methods and possibilities and is currently at the very beginnings of its potential. It also touches upon issues of privacy, politics, and generational divides.

In the broadest sense, personalization is marketing designed with the individual in mind. Obviously, there is no single human observing individual tastes to present products or services that are most relevant. This is where highly sophisticated algorithms come in.

One type of personalization is the recommendation engine, although it should be noted that personalization is not a type of recommendation. The most common of these are websites and streaming services, such as YouTube or Netflix. If you have ever used these sites you will know that certain box sets or videos are recommended based on previous viewing habits. To begin with, these were obviously connected programs.

For example, if you had watched a basketball documentary, there’s a good chance that one on baseball would interest you. So far, so simple. But, as recommendation engines become more sophisticated, seemingly unconnected content works just as well. The same type of algorithms are used in retail, most famously by Amazon, and include Email campaigns that are tailored to the individual and addressed personally. This is the beginning of personalization.

Personalization also uses segmentation, for example, individual traits, such as age, gender, or location, all of which can profoundly change how marketing information is presented. Beyond that, a person’s politics, browsing behavior, and even ethical concerns can be taken into account.

We should pause here to consider the two main versions of personalization, rule-based and machine learning.

Rule-based personalization relies on the previously mentioned segmentation model, whereby the audiences are broken down into both broad and granular segments, such as age or location.
Machine learning personalization, on the other hand, uses algorithms. These can be those used in recommendation engines and even parts of segmentation marketing, such as behavior.

Whereas basic algorithms can provide broad data, such as what is trending, recommendation engines, and the like, provide more in-depth information for the individual.

Personalization vs customization in e-commerce

Customization can come in many forms, from fast food restaurants that encourage customers to design their food their way, to online stores that allow visitors to design their own clothing.

Personalization, on the other hand, is a far more complex practice that takes into account customer (or potential customer) behavior in order to market the right products, to the right person, at the right time. Using big data and powerful algorithms, personalization is becoming a more powerful tool every day, with a diverse number of businesses implementing it to great success.

Getting the most out of both practices requires an understanding of how they both work and the major differences between them.

You can certainly see the similarities between customization and personalization:

Both aim to make consumers feel uniquely understood and marketed as individuals.
Both put a high value on self-expression and personal tastes and experiences.
Both create the illusion that consumers are being given white-glove treatment and have a one-on-one relationship with the brand.
Both, in a way, involve the consumer as a co-producer of value – for customization, it means relying on their predilections to craft the product, and for personalization, it’s sharing their data (unbeknownst to them or not) to create personalized marketing messages.

Ultimately, through a product strategy on the part of the company, mass customization is driven by the consumer. Especially appealing to Millennials, it’s a way of validating their own sense of self through ‘build-a-bear’ style product production.

With personalization, the onus is much more on the company to bring value and deliver meaning to the consumer audience they’re targeting.

People naturally crave to be in control of their surroundings and personalization techniques create a cherry-picked environment that feeds into that need. Bargain hunting for low-priced kitchenware from your favorite outlet? If ads for the very items you’re looking for suddenly appear all over the internet, it somehow creates a feeling of empowerment, as if in some impossible way, your wishes and needs automatically manifest.

As for information overload, this is the classic argument that contemporary consumers are exposed to a dizzyingly high number of marketing messages, far too many to consciously register, remember, or act on. Personalization strategies both (hopefully) limit the number of marketing messages a consumer is exposed to in the first place, as well as stimulate the brain to recognize these stand-out strategies, as opposed to letting them sit in the background of our consciousness. This is the idea of selective attention or the fact that your brain will automatically pick up on potentially important stimuli – i.e. those most relevant to you.

Web Personalization And Privacy Concerns: Customization and Personalization

Privacy is a major concern for many consumers and personalization has sometimes seemed more like an unwelcome stalker to some as a result. With legislation being debated in various corners of the world, what is deemed as overstepping the mark should be a concern for all.

All generations have some concerns regarding personalization and privacy, not helped by various data leaks and hacks of major companies. The truth of the matter, however, is that personalization shouldn’t be “creepy.” Transparency is a great way to instil trust with customers. Personalization need not be a back-door practice, where using personal data is hidden behind jargon and misleading declarations. Most website visitors are happy to share data as long as they know how it’s being used, especially if there is some advantage in it for them.

Being able to opt-out is another great way to make customers feel as if they have some control over the process. In some cases, the personalization process itself can be customized. While most users won’t bother engaging, they will appreciate the option to.

Evolution From Mass Marketing To One-To-One Marketing

Mass marketing is where a product or service is marketed to an entire population. It essentially treats everyone the same, with the same needs. Although that is clearly not the case, the philosophy revolves around the idea that the more people who receive the message, the more likely you will reach someone who is interested. It’s essentially the practice of selling low-cost and homogenous items at high volumes. While it’s more miss than hit, mass marketing was well suited to mass media markets, such as television, which had the majority of the population engaging with it on a regular basis.

Mass marketing began in the 1920’s, with the advent of radio. The popularity of this form of media made it ripe for advertisers to market products in a way that wasn’t possible before. As attitudes shifted over the decades, mass marketing’s influence rose and fell until the 1980’s and 1990’s, when it reached its peak.

History of mass marketing timeline:

1920’s – Begins with the advent of radio
1930’s – The great depression reduces its influence
1940’s and 1950’s – With income rising, its effectiveness becomes relevant again through the “Mad men” era
1960’s and 1970’s – A rise in anti-Capitalism sees its influence wane again
1980’s and 1990’s – The peak years of mass marketing during the economic boom

The history of one-to-one marketing is essentially a history of the Internet. When the first HTML dialogue occurred on Christmas Day, 1990, it set in motion the beginnings of personalization. Also in 1993, Webtrends was founded, which was essentially the first commercial web analytics program. Unfortunately, only those well-versed with the technology had any idea of how to read the data, so its effects were minimal.

Things carried on at pace, however, and log file analysis made it possible for non-tech people, most importantly marketers, to make use of the data. This was soon followed by hit counters and Javascript tagging, which became important as the Internet began to use more imagery. With few people using the Internet, however, the advances made during this time were not to be truly helpful for a few years to come.

It wasn’t until 2004 that the type of web analytics we know today began to appear and by 2005 Google had released Google Analytics. This allowed website owners to dig further into the data than had been previously possible, with concise visuals that allowed for easy reading of in-depth information. It’s at this point that personalization becomes more tangible, with conversion rate optimization becoming a particular focus.

Machine learning personalization, such as recommendation engines, soon began to be useful in a way that was not possible before, as algorithms began to exponentiate their capabilities, with Amazon and Netflix leading the way. From Email campaigns to accurate predictions of preferences, the practices of personalization became ubiquitous by 2008.

Mouse tracking and eye tracking also added profound data that improves visitor experience and thus increases interaction. With a deeper understanding of customer habits, personalization is beginning to become more accurate, focused, and effective.

With the advent of multiple devices using the Internet, Google released Universal Tracker in 2012. With more profound data at its fingertips, demographics, behavior, and lifestyle began to be segmented more accurately, further categorizing customers for more predictable results. App personalization becomes more and more important as phone use begins to outstrip laptops/desktops for online use. Machine learning on mobile soon improves.

Soon after, personalization magnifies the effectiveness of CRM (Customer Relation Management), which focuses on user experience and customer retention. This only becomes possible as big data is collected at ever higher rates, allowing companies to truly understand their customer’s needs.

With the use of A/B testing, the future of personalization is now highly managed from beginning to end. No longer is trial and error at the forefront of designers of websites or marketing campaigns. Behavior on site is monitored to a level thought unimaginable just a few years ago and personalization is becoming truly individualized. User experience is now at the heart of personalization, and with the likelihood of more powerful algorithms and customer understanding to come, personalization seems to be very much in its infancy.

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How to start your personalization strategy

Website personalization shouldn’t be a complicated undertaking. In fact, it is becoming easier, and therefore more widespread, every day. With several tools at a business’s disposal, there are many ways to go about creating personalization that works for each business. In other words, personalization should be personal for each business using it.

The first place to begin is getting to know an audience. Too many start with the concept that the product is key and then try to persuade an audience that they are right. This is similar to a waiter insisting that the customer has made the wrong order when they chose the duck and bringing them beef instead. As should be clear now, customer experience has become one of the most important aspects of personalization, and that cannot be achieved without getting to know exactly what it is your customer expects from the interaction with your website.

Want more on personalization? Read our E-book: Your Guide to Personalization

What’s Better: Personalization or Customization?

You want your customers to feel unique and have a positive experience with your brand. For you, this could mean implementing personalization tactics or dabbling into the world of product customization. Bettering your customers’ experience could mean focusing on customization, personalization, or a mixture of both. If you design your roadmap with your customers in mind, you’ll find customer loyalty and satisfaction along the way.

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Jun 3, 2024

16min read

5 Behavioral Targeting Tactics to Boost Conversions (with Examples)

AB Tasty

We’re in an era of banner blindness.

People increasingly ignore irrelevant ads while being more receptive to tailored online experiences that speak to their needs and wants. To keep a competitive edge, marketers need to focus on crafting personalized content and user experiences to increase their ad engagement and boost revenue.

Welcome to the world of behavioral targeting.

Let’s talk about how behavioral targeting is done, what data it involves, six examples of brands that are killing it with behavioral targeting, and some best practices to follow.

What is behavioral targeting?

Behavioral targeting is a marketing technique that segments audiences based on behaviors rather than just demographic parameters.

Simply put, website owners can use the data collected from user behavior to create profiles and hyper-target future advertising for specific groups of customers. Behavioral targeting allows brands and marketers to engage customers and rise above more traditional strategies.

Modern marketers use behavioral targeting to achieve greater engagement in an era where more and more online shoppers have developed strong avoidance habits toward most ad formats.

What data do you need for behavioral targeting in marketing and advertisement?

Behavioral targeting campaigns are data-driven. Behavioral data is often collected with:

Your company’s web analytics tools
Collected cookies
Customers’ browsing history
Collected IP addresses

The most common metrics collected for behavioral targeting are:

Geographic location
Type of devices used
Visit data
Transactional data
Purchase history
Browsing history

Basically, marketers use any type of data—provided that it delivers actionable insights—that can be used to increase engagement and conversions during a campaign.

The benefits of behavioral marketing and behavioral advertising

Relevancy: By analyzing a customer’s past behavior, you can create more relevant ads to give users a better (and less spammy) experience.
Efficiency: Targeting users who’ve shown an interest in your products/services ensures you spend your budget more efficiently.
Improved ROI: Increasing the relevancy and efficiency of ads will, in turn, lead to a better ROI.

Behavioral targeting examples

Retargeting ads examples

You can use retargeting advertisements to show advertisements to your website users that are tailored to their actions on your website. Both Facebook and Google offer retargeting adverts. Retargeting adverts are an excellent way to encourage a website visitor to return to your website by showing the relevant ad based on their past behavior. Here’s how a couple of large brands use retargeting adverts to increase sales:

Neutrogena

Neutrogena, a well-known beauty brand, used customers’ past shopping cart behavior to increase sales. Knowing that 75% of its customers were purchasing products from one segment of its range, Neutrogena decided to take action to increase the number of products purchased by existing customers.

The company used historic shopping cart data to create product pairings: products that went well together and also reflected historic customer buying patterns — think mascara and eye makeup remover, for example.

neutrogena-behavioral

Armed with customers’ purchase behaviors, Neutrogena created banner advertisements and videos displaying product pairing, product information, and – last but not least – coupons to encourage sales.

Finally, these product pairing adverts were displayed to customers based on their past purchasing patterns. The results speak for themselves. Neutrogena got a £5.84 return on behavioral advertising spend (ROAS) and exceeded its own benchmark by 289%.

Aurum Brothers

This luxury male watch brand used its users’ website behaviors and Facebook retargeting advertisements to increase sales and brand awareness.

The campaign segmented the company’s existing website users into three groups:

People who had added an item to their baskets
People who had viewed specific items
People who had visited the website

As well as designing specific adverts for each group, Aurum Brothers tested different ad settings such as bid options and ad objectives.

aurum-brothers-targeting

Facebook retargeting based on customer behavior was highly successful for the company. They reported 100% increases month on month and an increase of 50% in revenue.

Behavioral Email Marketing

One example of behavioral marketing is behaviorally targeted email campaigns. Email campaigns can be triggered by actions taken on a website, such as subscribing to a newsletter, adding an item to the cart, or viewing the sales page.

Here’s an example of behavioral email marketing in real life:

Closet London

A clever way to use behavioral targeting is to segment your customers based on their stage in the buying cycle, and then retarget them with email campaigns specific to their shopping journey.

And that’s exactly what clothing brand Closet London did.

The company split its customer base into four groups based on their past purchases and implemented email marketing workflows specific to each group. The groups were:

one-time purchases
repeat purchases
loyal customers
dormant customers

If a customer is categorized as a dormant customer, they will be sent an email about the latest collection. Then, if no conversion takes place within two weeks, the brand encourages the user to re-engage by emailing them a discount offer.

closet-london-targeting

But Closet London doesn’t stop there. The clever clothing brand also sends a variety of other email campaigns tailored to both new users (e.g. a welcome email campaign) and past customers (e.g. an email workflow based on the items they’ve purchased in the past).

If you’re concerned that too many emails may annoy your customers, don’t be.

By segmenting customers based on their actions on your website, you ensure that you’re sending well-timed, relevant, and useful emails to the correct segmentation of your customers. Do it successfully and you might get results like Closet London — an increase in revenue of 2900%.

Location-Based Advertising

By using location-based advertising (LBA) you can adapt your marketing message based on where your target consumers are geographically.

It even allows you to tailor your message based on the proximity to stores, the weather, transport routes, and so on. This means that you can create messages that make sense, given the location or the weather they are experiencing.

Here are some examples of brands using location-based advertising to generate sales and build brand awareness:

Timberland

Timberland wanted to drive a younger demographic of customers to visit its physical stores and stockists.

Timberland used a combination of data, including whether a user had recently visited a brick-and-mortar store and how close they were to a store at the time.

timberland-targeting

The brand used technology to draw polygons around Timberland stores to target people in the “mindset to purchase footwear.”

The campaign results showed an increase in-store visits by 6.2%, with, notably, 20% of these visits within 24 hours of the user viewing the advertising campaign.

Neutrogena (again)

We’ve already discussed how Neutrogena used customers’ past shopping cart behaviors to increase its sales, so we know that the beauty brand is no stranger to behavioral targeting.

However, its next strategy – to advertise a new sunscreen – was rather ingenious.

Not happy to only target customers based on weather forecast apps, Neutrogena used real-time UV conditions, the time of day, and the proximity to shops selling Neutrogena to target potential customers.

Imagine browsing your phone on an unexpectedly hot summer’s day. You flick through Facebook and see a Neutrogena advert. You head to your nearest store and, surprise, surprise, it sells Neutrogena.

Which sunscreen will you purchase? I’m going to bet it’s Neutrogena.

Again, the results are stellar. Within a couple of months, the campaign increased awareness of the sunscreen from zero to 63% and increased purchase intent to more than 40%.

Suggested Selling Examples

Suggested selling is simply offering choices based on items that customers have already purchased. Suggested selling can come in the form of upselling or cross-selling, neither of which are new to the retail world.

Amazon

No article about suggested selling would be complete without discussing Amazon, arguably the Godfather of this technique.

According to this source, more than a third of Amazon’s revenue comes from its recommendation engine. That’s massive, but how does it work? Well, in a handful of ways.

amazon-targeting

Recommended for You

On Amazon’s home page, you can click on a “Your Recommendations” link. This directs you to a page full of products recommended just for you. By suggesting a selection of products from the categories you’ve already viewed, Amazon aims to encourage you to click and buy additional items.

Frequently Bought Together

By adding a ‘frequently bought together’ section below your cart, Amazon successfully manages to increase your order value.

Browsing History

Amazon also shows you a history of the items you’ve purchased on Amazon. The fact that you’ve already viewed it signals that you’ve previously been interested in purchasing it, so it’s an easy way for Amazon to remind you of the product.

Sunuva

Sunuva may be a less well-known brand on the list, but its use of behavioral targeting has generated excellent results.

This UK-based kid’s clothing company wanted to increase sales, but with a small team, whatever the solution, it needed to be automated and easy to implement.

One of the core elements to increase sales was to focus on and reduce cart abandonment rates.

sunuva-targeting

After a website redesign, Sunuva was able to use browsing behavior and real-time crowd-sourced data from other visitors.

This enabled the company to present its website visitors with relevant product recommendations, as well as email campaigns with content tailored to the customer, instead of generic offers.

Remarkably, the changes increased turnover by nearly 9% from the very first day.

Why is behavioral targeting slowly replacing demographic targeting?

Demographic data is limited.

Age, location, and income are all great factors in helping marketers create targeted messages. However, demographic data is fairly restricted when it comes to understanding the needs, wants, habits, and pain points of your customers.

Demographic data won’t tell you much about your customers’ behavior. Using strictly demographic data is often a hit-or-miss game.

Using behavioral data, marketers can target their own visitors by knowing which pages they’ve visited and what they’ve left in their carts. It allows for extremely precise targeting that cannot be achieved using demographic data.

Getting customers’ attention is harder than ever.

With more and more people ignoring generic ad formats, marketers worry that traditional PPC advertising and display ads are losing momentum.

Demographic data is used by everyone

Most demographic data can be accessed by anyone, including your competitors.

To keep their edge, marketers should use their own customer’s data to create more personalized online experiences. That way, marketers can achieve greater ROAS and ROI while ensuring their customers are exposed to the right ads, at the right time.

The next step of behavioral marketing: emotional personalization

Emotions play a pivotal role in every step of the buying process. To truly connect with consumers, brands must decode not just their behaviors, but also the emotional motivations behind their decisions. Purchasing decisions are not always rational, and not everyone reacts in the same way.

With AB Tasty’s new hyper personalization software, EmotionsAI, you can craft tailored messages for each visitor type, analyze data to discern their desires, conduct experiments to refine messaging and design personalized journeys that cater to specific emotional triggers.

Stay ahead of the curve in experience optimization with EmotionsAI – the ultimate tool for mastering emotional personalization. Dive into emotional personalization with sophisticated algorithms to anticipate buying patterns and tailor experiences accordingly.

5 Behavioral targeting marketing and advertisement tactics

Leverage upselling & cross-selling

Knowing what your customers love and how they interact with your business is a massively powerful tool to suggest additional products to them.

Take Spotify. They track the music we listen to and the frequency at which we do it, and then craft personal ads based on our preferences to sell concert tickets and bring us back to their app. Behavioral marketing is that powerful.

Spotify uses customer data

If your company has any e-commerce activities, then you’re likely already familiar with cross-selling and suggested products: techniques that are also powered by behavioral marketing.

Macy uses product recommendations

As an example, Macy’s uses product recommendations to promote related products based on customers’ data, to increase basket value.

2. Use behavioral email marketing campaigns

According to FPS research insights, email marketing still delivers the highest conversion rates when it comes to selling products and services.

FPS research insights

Knowing this, marketers can strengthen their email marketing campaigns by using behavioral targeting tactics.

Basically, behavioral email marketing consists of sending targeted emails to users based on their past actions on a website (cart abandonment, pages visited, newsletter subscription, etc.).

Take this example: Quora’s goal is for you to return to their website as much as possible.

Quora uses behavioral marketing to draw back users

By knowing which pages you’ve read in the past, Quora is able to send personalized emails highlighting similar topics to pique your interest and draw you back to their site.

This is behavioral targeting on an individual scale.

3. Leverage Facebook, Google, and other retargeting services

Retargeting and remarketing are common tactics used to target potential customers who’ve previously visited your website by showing them ads on other websites (like online publications, social networks, or even game sites).

There are several ad networks that support retargeting.

Among them, Facebook and Google are the most common options because they reach large audiences and provide accurate data and analysis on the generated sales. They also boast a lot of integrations with third-party data analysis tools.

Nowadays, the number of factors that can be tracked is impressive:

Which pages have been visited?
How long were the sessions?
Which products were bought?
What was the average order value?
How many products were purchased?
How long has it been since a visitor’s last session?
Which customers have added a product to the cart and then abandoned it?

Once marketers have gathered enough behavioral data, they can proceed to create user segments based on behavioral traits and show them highly relevant ads.

Here’s an example of retargeting:

Let’s say your e-commerce generates high cart abandonment rates.

You can create a user segment based on people who have abandoned a specific product (say, your best seller) in their cart and create an ad that will target these users. To increase its efficiency, you can create a sense of urgency by offering them a discount provided that they buy the item now.

If you successfully target the right people, your ad’s audience is now exclusively composed of potential customers who already know your product, thus generating much higher conversion rates.

Although we’ve talked a lot about Facebook and Google’s retargeting features, do not forget that other advertising platforms (like Outbrain or Criteo) can also provide remarketing services.

A retargeted ad appears on Forbes

Your retargeted ads can appear on many websites, including major online publications such as Forbes or WSJ, depending on your audience’s habits and digital media consumption.

4. Go granular with precise geographic targeting

Whether you’re selling products or services, knowing the precise geolocation of your visitors (thanks to their IP addresses) can make a huge difference in your campaign’s success.

Let’s pretend that you run a clothing company that sells year-round fashion. Using your data analytics tool, you could create user segments based on their geolocation to advertise clothes that are relevant to them, given their current browsing location.

Geo-targeted ads can also be served at a city level, meaning that marketers can tailor ads to reach a restricted but qualified audience. This can be especially useful for companies that rely on their respective offices to carry out their business activities.

Using geo-targeted advertising, marketers are able to create specific, tailored audiences that leverage both behavioral and demographic parameters to ensure their campaign’s success.

5. Experiment with personalized coupons, offers, and discounts

Website personalization consists of crafting customized experiences based on consumers’ wants, needs, and past actions as opposed to offering a single, generic experience to all consumers regardless of their preferences.

Retail and tech giants like Amazon have long started to implement some level of website personalization (like wishlists and recommended products). Displaying different content based on a visitor’s personal preferences has become an essential marketing technique.

People don’t hate ads, they hate irrelevant ads.

Knowing this, marketers can create segment-based ads to increase relevancy and boost engagement.

targeted popup ad

This targeted pop-up ad has an offer enclosed to deter users from leaving the site. By using an all-in-one CRO solution (like AB Tasty) you can implement customized content on any page you want and craft your own display rules based on your consumers’ data.

How to create a personalized experience

Our team at AB Tasty knows how much of an impact customized experiences can make on our clients’ online revenue. So, we implemented a loyalty overlay pop-up for one of our French fashion retailers. This overlay pop-up would only appear for loyal customers and reward them with a limited discount.

Our goal was to increase customer retention while maximizing revenue from returning customers, boosting brand loyalty in the very competitive French fashion environment.

popup image

Getting Started with Behavioral Marketing

Behavioral marketing is a powerful way to carry out your marketing strategy. It uses the behaviors of your website visitors and customers to create highly relevant content that encourages them to make a purchase at your website or even in your online store.

This article has discussed some examples of brands successfully using behavioral marketing, but now it’s over to you to try it out for yourself.

Start by choosing one of the tactics shown in this article and see how it can make your advertising more relevant and successful.

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AB Tasty is now available on the Shopify App Store

AB Tasty

We’re excited to share that AB Tasty is now available on the Shopify app store. This means it’s easier to use AB Tasty’s leading experimentation and personalization solutions directly on Shopify sites.

The launch marks an important milestone for the partnership between AB Tasty and Shopify, providing a more seamless user experience and keeping experience optimization accessible with easy-to-use solutions.

What this means for Shopify merchants

Brands using Shopify can enhance their commerce sites with AB Tasty to boost conversions and optimize experiences. Set-up is simple: search for AB Tasty in the Shopify App Store and install the tag in just three steps.

Once a site is equipped with AB Tasty, you can easily access your favorite features, run tests, and personalize content throughout the shopping funnel from homepage to cart page.

Building better experiences on your Shopify sites is also easy with AB Tasty’s ready-to-use widget library including interactive features like the scratch card. Or you can create your own custom widget.

When it’s time to check on how your campaigns are performing, track your success with analytics that automatically link Shopify transactions(transaction rate, total number of transactions, average basket, items per transaction, average item price per transaction, etc.) and other transaction data (like currency, coupons, payment method, etc.) to your AB Tasty campaigns. Quickly identify what campaigns work for your audience and where you can make adjustments.

How does it work?

When you are ready to get started, connect AB Tasty with your Shopify site in three steps.

Install the AB Tasty app directly from the Shopify app store.
Enable the extension with your AB Tasty identifier.
Hit save.

Now you can get to work building better experiences for your visitors. Really, that’s it.

Over 100 brands already use AB Tasty & Shopify to optimize their sites

Learn more about how Embark Veterinary’s e-commerce teams use AB Tasty’s experimentation solution to test product copy and increase revenue per session and conversion rate.

To wrap up

At AB Tasty, we’re your optimization partners helping ignite change from the inside out. That’s why we’re continuously improving the experience of our customers, from new integrations to strengthened partnerships and beyond.

To connect AB Tasty to your Shopify site, get started here.

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CX Optimization Webseries APAC: Episode #3 – The Importance of Continuous Optimization in A/B Testing

Ross Salveron

Testing as well is such a benefit from de-risking that decision making.
– Tom Shepherd, UX Lead at David Jones

Hosted by Serena Ku, Senior CSM at AB Tasty

Featuring Tom Shephard, UX Lead at David Jones

In the fast-paced world of digital commerce, A/B testing and continuous optimization are important processes allowing brands to refine strategies, improve customer experiences, and increase conversion rates over time.

One huge pitfall many businesses face is they look at what their competitors are doing and assume that it will work for them too. But remember, things are not always as they appear.

In this third episode of our CX Optimization Web Series, Tom Shepherd, UX Lead at David Jones joins Serena Ku, Senior Customer Success Manager at AB Tasty to discuss the importance of Continuous Optimization in A/B Testing.

Discover how a business perspective can shift from “we think” to “we know”.

Episode #3:

Why is it important for brands to run A/B tests?

The main benefits are improved content engagement, increased conversion rates and reduced bounce rates.

If you’re not A/B testing, you may already be behind your direct competitors. This by itself is a compelling motivation for why brands should start testing. Speeding up the time it takes to bring an idea or a concept to market is another benefit worth considering A/B testing.

Take note, businesses need to level up and be able to keep up with behavioral changes and look for opportunities where experiences are not achieving the results they should be.

The Role of AB Tasty to empower David Jones’ CRO strategy

In a traditional UX setting, it is quite frustrating when you invest a lot of time mocking up experiences, taking those to customers, and later finding out that they just don’t work.

The Australian luxury department store, David Jones, takes experience optimization seriously. They look closely to understand their customers in all facets. Using GA4 and FullStory, they can draw out ideas and build solutions that will make an experience more seamless, removing friction. With AB Tasty, they launch these experiences quickly and expose them to their customers to gather valuable insights.

As a discipline within the user experience team, David Jones leverages AB Tasty and analytics tools to marry quantitative data with qualitative insights delighting every customer.

Winning customer loyalty

Customer loyalty is all about the experience. Its essence in the e-commerce landscape is where the digital store has made each customer feel highly valued.

Perfecting the art of customer loyalty requires both creativity and precision.–That is why, like your local store attendant, EmotionsAI helps brands understand the emotional needs of audiences to bolster your Experience Optimization roadmap with effective messages, designs and CTAs that activate your visitors.

Factors to consider when testing?

Truly knowing your customer demographics and understanding their behaviors online will allow you to create a well-formulated hypothesis. Consider the time of the year when you launch a test. Is it an off-peak season, are you running promotions, or clearing stocks? Analyze your data and focus on where your conversion points are.

Tom suggests iterating and running as many follow-up tests as possible. If you tested something that worked, you might be up to something even greater. So test more iterations to unlock more results.

The wrap:

The strongest path to customer loyalty, higher conversion, and a customer base nobody can touch is having ‘differentiated experiences’. Start with a deeper knowledge of your industry and beyond. Know your customers and empathize with them. Be mindful that behaviors and preferences are ever-changing. Continuous optimization helps you adapt, execute strategies, and stay ahead of the game.

Dare to be better

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Mutually Exclusive Experiments: Preventing the Interaction Effect

Hubert Wassner

What is the interaction effect?

If you’re running multiple experiments at the same time, you may find their interpretation to be more difficult because you’re not sure which variation caused the observed effect. Worse still, you may fear that the combination of multiple variations could lead to a bad user experience.

It’s easy to imagine a negative cumulative effect of two visual variations. For example, if one variation changes the background color, and another modifies the font color, it may lead to illegibility. While this result seems quite obvious, there may be other negative combinations that are harder to spot.

Imagine launching an experiment that offers a price reduction for loyal customers, whilst in parallel running another that aims to test a promotion on a given product. This may seem like a non-issue until you realize that there’s a general rule applied to all visitors, which prohibits cumulative price reductions – leading to a glitch in the purchase process. When the visitor expects two promotional offers but only receives one, they may feel frustrated, which could negatively impact their behavior.

What is the level of risk?

With the previous examples in mind, you may think that such issues could be easily avoided. But it’s not that simple. Building several experiments on the same page becomes trickier when you consider code interaction, as well as interactions across different pages. So, if you’re interested in running 10 experiments simultaneously, you may need to plan ahead.

A simple solution would be to run these tests one after the other. However, this strategy is very time consuming, as your typical experiment requires two weeks to be performed properly in order to sample each day of the week twice.

It’s not uncommon for a large company to have 10 experiments in the pipeline and running them sequentially will take at least 20 weeks. A better solution would be to handle the traffic allocated to each test in a way that renders the experiments mutually exclusive.

This may sound similar to a multivariate test (MVT), except the goal of an MVT is almost the opposite: to find the best interaction between unitary variations.

Let’s say you want to explore the effect of two variation ideas: text and background color. The MVT will compose all combinations of the two and expose them simultaneously to isolated chunks of the traffic. The isolation part sounds promising, but the “all combinations” is exactly what we’re trying to avoid. Typically, the combination of the same background color and text will occur. So an MVT is not the solution here.

Instead, we need a specific feature: A Mutually Exclusive Experiment.

What is a Mutually Exclusive Experiment (M2E)?

AB Tasty’s Mutually Exclusive Experiment (M2E) feature enacts an allocation rule that blocks visitors from entering selected experiments depending on the previous experiments already displayed. The goal is to ensure that no interaction effect can occur when a risk is identified.

How and when should we use Mutually Exclusive Experiments?

We don’t recommend setting up all experiments to be mutually exclusive because it reduces the number of visitors for each experiment. This means it will take longer to achieve significant results and the detection power may be less effective.

The best process is to identify the different kinds of interactions you may have and compile them in a list. If we continue with the cumulative promotion example from earlier, we could create two M2E lists: one for user interface experiments and another for customer loyalty programs. This strategy will avoid negative interactions between experiments that are likely to overlap, but doesn’t waste traffic on hypothetical interactions that don’t actually exist between the two lists.

What about data quality?

With the help of an M2E, we have prevented any functional issues that may arise due to interactions, but you might still have concerns that the data could be compromised by subtle interactions between tests.

Would an upstream winning experiment induce false discovery on downstream experiments? Alternatively, would a bad upstream experiment make you miss an otherwise downstream winning experiment? Here are some points to keep in mind:

Remember that roughly eight tests out of 10 are neutral (show no effect), so most of the time you can’t expect an interaction effect – if no effect exists in the first place.
In the case where an upstream test has an effect, the affected visitors will still be randomly assigned to the downstream variations. This evens out the effect, allowing the downstream experiment to correctly measure its potential lift. It’s interesting to note that the average conversion rate following an impactful upstream test will be different, but this does not prevent the downstream experiment from correctly measuring its own impact.
Remember that the statistical test is here to take into account any drift of the random split process. The drift we’re referring to here is the fact that more impacted visitors of the upstream test could end up in a given variation creating the illusion of an effect on the downstream test. So the gain probability estimation and the confidence interval around the measured effect is informing you that there is some randomness in the process. In fact, the upstream test is just one example among a long list of possible interfering events – such as visitors using different computers, different connection quality, etc.

All of these theoretical explanations are supported by an empirical study from the Microsoft Experiment Platform team. This study reviewed hundreds of tests on millions of visitors and saw no significant difference between effects measured on visitors that saw just one test and visitors that saw an additional upstream test.

Conclusion

While experiment interaction is possible in a specific context, there are preventative measures that you may take to avoid functional loss. The most efficient solution is the Mutually Exclusive Experiment, allowing you to eliminate the functional risks of simultaneous experiments, make the most of your traffic and expedite your experimentation process.

References:

https://www.microsoft.com/en-us/research/group/experimentation-platform-exp/articles/a-b-interactions-a-call-to-relax/

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Tapping into Emotional Marketing | Talia Wolf

Maddie Ostrander

Talia Wolf reveals how emotional marketing can revolutionize your experimentation process and lift conversions.

Taking a customer-centric approach to marketing, founder and CEO of Getuplift, Talia Wolf, harnesses the power of emotional marketing techniques to increase visitor conversions.

Her natural interest in conversion rate optimization (CRO) and experimentation was sparked through her early work in a social media agency, later moving on to become an expert in the field – consulting for many companies on the subject, and speaking on stage at Google, MozCon and Search Love.

Guest host and AB Tasty’s Head of Growth Marketing UK, John Hughes, spoke with Talia about emotional marketing as a tool for optimization, delving into how customer research can facilitate the experimentation process, reduce the rate of failure, and earn the buy-in from company stakeholders.

Here are some of the key takeaways from their conversation.

What is emotional marketing?

Based upon the idea that emotion drives every single decision that we make in life, the emotional targeting methodology shifts the focus of your online marketing content from your solution, features, or pricing, to your customer. Rather than playing a guessing game and simply reshuffling elements on a page, this technique requires a deeper understanding of human behavior. By identifying customer intent and buying motivation, you can create an optimized experience, which meets their needs and increases conversions.

Backed by academic research, the fundamental role of emotion in our daily choices can be integrated into your strategy to better cater to your customers by figuring out a) their biggest challenges and, b) how they want to feel after finding a solution. What is their desired outcome?

With this in mind, you can optimize your digital communications with high-converting copy and visuals that speak directly to your customers’ needs. By shifting the conversation from the product to the customer, an incredible opportunity opens up to scale and multiply conversions.

How do you build and measure an emotion-based experiment?

Firstly, experimentation should be backed by research. From customer and visitor surveys, to review mining, social listening and emotional competitor analysis, Talia encourages extensive research in order to create the most likely hypothesis upon which to base an A/B test.

Once you know more about your customers, you can review the copy and visuals on your product page for example, and from your research you might discover that your content is not relevant to your target customer. You can then come up with a hypothesis based on their actual needs and interests supported by compelling social proof, and write a brief for your designer or copywriter based on the new information.

From there you can build your experiment into your A/B testing platform with a selected North star metric, whether it’s check-outs, sign-ups or add-to-carts, to prove or disprove your hypothesis. And, while we know that nine out of 10 A/B tests fail, emotional marketing facilitates the hypothesizing process, strengthening the chance of creating a winning experiment by testing variables that can actually impact the customer journey.

How to persuade stakeholders to support your experiments.

When it comes to CRO, there are often too many chefs in the kitchen, especially in smaller organizations where founders have a concrete vision of their customers and their messaging.

Talia explains that a research-based approach to experimentation can offer reassurance as part of a slow-and-steady strategy, backed by evidence. This personalized methodology involves talking to your customers and website visitors and scouring the web for conversations about your specific industry, rather than simply following your competitor’s lead.

It becomes a lot easier to propose a test to a founder or CEO when your hypothesis is supported by data and research, however, Talia recommends resisting the urge to change everything at once and rather, start small. Test the emotional marketing in your ads or send out an email sequence requiring only a copywriter, and share the results.

When you’re trying to get buy-in, you need to have a strong hypothesis paired with good research to prove that it makes sense. If this is the case, you can demonstrate the power of emotional marketing by running a couple of A/B tests: one where the control is the current solution-focused content and the variant is a customer-focused alternative, and another which highlights how customers feel right now versus how they want to feel – two important variations which help you to relate better to your customer. The key to garnering support is to take baby steps and continuously share your research and results.

What else can you learn from our conversation with Talia Wolf?

Why B2B purchases are more emotional than B2C. (15:50)
How to stand out in a crowded market by knowing your customer. (20:00)
How emotional marketing impacts the entire customer journey. (25:50)
How to relate to your customer and improve conversions. (32:40)

About Talia Wolf

Conversion optimization specialist Talia Wolf is the founder and CEO of Getuplift – a company that leverages optimization strategies such as emotional targeting, persuasive design, and behavioral data to help businesses generate more revenue, leads, engagement and sales.

Starting her career in a social media agency, where she was introduced to the concept of CRO, Talia went on to become the Marketing Director at monday.com, before launching her first conversion optimization agency, Conversioner, in 2013.

Today, with her proven strategy in hand, Talia teaches companies all over the world to optimize their online presence using emotional techniques.

About 1,000 Experiments Club

The 1,000 Experiments Club is an AB Tasty-produced podcast hosted by Marylin Montoya, VP of Marketing at AB Tasty. Join Marylin and the Marketing team as they sit down with the most knowledgeable experts in the world of experimentation to uncover their insights on what it takes to build and run successful experimentation programs.

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Frequentist vs Bayesian Methods in A/B Testing

Hubert Wassner

In A/B testing, there are two main ways of interpreting test results: Frequentist vs Bayesian.

These terms refer to two different inferential statistical methods. Debates over which is ‘better’ are fierce – and at AB Tasty, we know which method we’ve come to prefer.

If you’re shopping for an A/B testing vendor, new to A/B testing or just trying to better interpret your experiment’s results, it’s important to understand the logic behind each method. This will help you make better business decisions and/or choose the best experimentation platform.

Bayesian vs frequentist methods in ab testing

Source

In this article, we discuss these two statistical methods under the inferential statistics umbrella, compare and contrast their strong points and explain our preferred method of measurement.

What is inferential statistics?

Both Frequentist and Bayesian methods are under the umbrella of inferential statistics.

As opposed to descriptive statistics (which describes purely past events), inferential statistics try to infer or forecast future events.

Would version A or version B have a better impact on X KPI?

Side note: If we want to geek out, technically inferential statistics isn’t really forecasting in a temporal sense, but extrapolating what will happen when we apply results to a larger pool of participants.

What happens if we apply winning version B to my entire website audience? There’s a notion of ‘future’ events in that we need to actually implement version B tomorrow, but in the strictest sense, we’re not using statistics to ‘predict the future.’

For example, let’s say you were really into Olympic sports, and you wanted to learn more about the men’s swimming team. Specifically, how tall are they? Using descriptive statistics, you could determine some interesting facts about ‘the sample’ (aka the team):

The average height of the sample
The spread of the sample (variance)
How many people are below or above the average
Etc.

This might fit your immediate needs, but the scope is pretty limited.

What inferential statistics allows you to do is to infer conclusions about samples that are too big to study in a descriptive way.

If you were interested in knowing the average height of all men on the planet, it wouldn’t be possible to go and collect all that data. Instead, you can use inferential statistics to infer that average from different, smaller samples.

Two ways of inferring this kind of information through statistical analysis are the Frequentist and Bayesian methods.

What is the Frequentist statistics method in A/B testing?

The Frequentist approach is perhaps more familiar to you since it’s more frequently used by A/B testing software (pardon the pun). This method also makes an appearance in college-level stats classes.

This approach is designed to make a decision about a unique experiment.

With the Frequentist approach, you start with the hypothesis that there is no difference between test versions A and B. And at the end of your experiment, you’ll end up with something called a P-Value (probability value).

The P-Value is the probability of obtaining results at least as extreme as the observed results assuming that there is no (real) difference between the experiments.

In practice, the P-Value is interpreted to mean: the probability that there is no difference between your two versions. (That’s why it is often “inverted” with the basic formula p = 1-pValue, in order to express the probability that there is a difference.)

The smaller the P-Value, the higher the chance that there is, in fact, a difference, and also that your hypothesis is wrong.

Frequentist pros:

Frequentist models are available in any statistic library for any programming language.
The computation of frequentist tests is blazing fast.

Frequentist cons:

You only estimate the P-Value at the end of a test, not during. ‘Data peeking’ before a test has ended generates misleading results because it actually becomes several experiments (one experiment each time you peek at the data), whereas the test is designed for one unique experiment.
You can’t know the actual gain interval of a winning variation – just that it won.

What is the Bayesian statistics method in A/B testing?

The Bayesian approach looks at things a little differently.

We can trace it back to a charming British mathematician, Thomas Bayes, and his eponymous Bayes’ Theorem.

Bayes Theorem

Source

The Bayesian approach allows for the inclusion of prior information (‘a prior’) into your current analysis. The method involves three overlapping concepts:

Prior – information you have from a previous experiment. At the beginning of the experiment, we use a ‘non-informative’ prior (think ’empty’)
Evidences – the data of the current experiment
Posterior – the updated information you have from the prior and the evidences. This is what is produced by the Bayesian analysis.

By design, this test can be used for an ongoing experiment. When data peeking, the ‘peeked at data’ can be seen as a prior, and the future incoming data will be the evidence, and so on.

This means ‘data peeking’ naturally fits in the test design. So at each ‘data peeking,’ the posterior computed by the Bayesian analysis is valid.

Crucially for A/B testing in a business setting, the Bayesian approach allows the CRO practitioner to estimate the gain of a winning variation – more on that later.

Bayesian pros:

Allows you to ‘peek’ at the data during a test, so you can either stop sending traffic if a variation is tanking or switch earlier to a clear winner.
Allows you to see the actual gain of a winning test.
By its nature, often rules out the implementation of false positives.

Bayesian cons:

Needs a sampling loop, which takes a non-negligible CPU load. This is not a concern at the user level, but could potentially gum things up at scale.

Bayesian vs Frequentist: which is better?

So, which method is the ‘better’ method?

Let’s start with the caveat that both are perfectly legitimate statistical methods. But at AB Tasty, our customer experience optimization and feature management software, we have a clear preference for the Bayesian a/b testing approach. Why?

Gain size

One very strong reason is because with Bayesian statistics, you can estimate a range of the actual gain of a winning variation, instead of only knowing that it was the winner, full stop.

In a business setting, this distinction is crucial. When you’re running your A/B test, you’re really deciding whether to switch from variation A to variation B, not whether you choose A or B from a blank slate. You therefore need to consider:

The implementation cost of switching to variation B (time, resources, budget)
Additional associated costs of variation B (vendor costs, licenses…)

As an example, let’s say you’re a B2B software vendor, and you ran an A/B test on your pricing page. Variation B included a chatbot, whereas version A didn’t. Variation B outperformed variation A, but to implement variation B, you’ll need 2 weeks of developer time to integrate your chatbot into your lead workflow, plus allocate X dollars of marketing budget to pay for the monthly chatbot license.

via GIPHY

You need to be sure the math adds up, and that it’s more cost-effective to switch to version B when these costs are weighed against the size of the test gain. A Bayesian A/B testing approach will let you do that.

Let’s take a look at an example from the AB Tasty reporting dashboard.

In this fictional test, we’re measuring three variations against an original, with ‘CTA clicks’ as our KPI.

AB Tasty reporting

We can see that variation 2 looks like the clear winner, with a conversion rate of 34.5%, compared to the original of 25%. But by looking to the right, we also get the confidence interval of this gain. In other words, a best and worst-case scenario.

The median gain for version 2 is 36.4%, with the lowest possible gain being +2.25% and the highest being 48.40%

These are the lowest and the highest gain markers you can achieve in 95% of cases.

If we break it down even further:

There’s a 50% chance of the gain percentage lying above 36.4% (the median)
There’s a 50% chance of it lying below 36.4%.
In 95% of cases, the gain will lie between +2.25% and +48.40%.
There remains a 2.5% chance of the gain lying below 2.25% (our famous false positive) and a 2.5% chance of it lying above 48.40%.

This level of granularity can help you decide whether to roll out a winning test variation across your site.

Are both the lowest and highest ends of your gain markers positive? Great!

Is the interval small, i.e. you’re quite sure of this high positive gain? It’s probably the right decision to implement the winning version.

Is your interval wide but implementation costs are low? No harm in going ahead there, too.

However, if your interval is large and the cost of implementation is significant, it’s probably best to wait until you have more data to shrink that interval. At AB Tasty we generally recommend that you:

Wait until you have recorded at least 5,000 unique visitors per variation
Let the test run for at least 14 days (two business cycles)
Wait until you have reached 300 conversions on the main goal.

Data peeking

Another advantage of Bayesian statistics is that it’s ok for you to ‘peek’ at your data’s results during a test (but be sure not to overdo it…).

Let’s say you’re working for a giant e-commerce platform and you’re running an A/B test involving a new promotional offer. If you notice that version B is performing abysmally – losing you big money – you can stop it immediately!

Conversely, if your test is outperforming, you can switch all of your website traffic to the winning version earlier than if you were relying on the Frequentist method.

This is precisely the logic behind our Dynamic Traffic Allocation feature – and it wouldn’t be possible without Mr. Thomas Bayes.

Dynamic Traffic Allocation

If we pause quickly on the topic of Dynamic Traffic Allocation, we’ll see that it’s particularly useful in business settings or contexts that are volatile or time-limited.

AB Tasty dynamic traffic allocation bayesian

Dynamic Traffic Allocation option in the AB Tasty Interface.

Essentially, (automated) Dynamic Traffic Allocation strikes the balance between data exploitation and exploration.

The test data is ‘explored’ rigorously enough to be confident in the conclusion, and ‘exploited’ early enough so as to not lose out on conversions (or whatever your primary KPI is) unnecessarily. Note that this isn’t manual – a real live person is not interpreting these results and deciding to go or not to go.

Instead, an algorithm is going to make the choice for you, automatically.

In practice, for AB Tasty clients, this means checking the associated box and picking your primary KPI. The platform’s algorithm will then make the determination of if or when to send the majority of your traffic to a winning variation, once it’s determined.

This kind of approach is particularly useful:

Optimizing micro-conversions over a short time period
When the time span of the test is short (for example, during a holiday sales promotion)
When your target page doesn’t get a lot of traffic
When you’re testing 6+ variations

Though you’ll want to pick and choose when to go for this option, it’s certainly a handy one to have in your back pocket.

Want to start A/B testing on your website with a platform that leverages the Bayesian method? AB Tasty is a great example of an A/B testing tool that allows you to quickly set up tests with low code implementation of front-end or UX changes on your web pages, gather insights via an ROI dashboard, and determine which route will increase your revenue.

False Positives

In Bayesian statistics, like with Frequentist methods, there is a risk of what’s called a false positive.

A false positive, as you might guess, is when a test result indicates a variation shows an improvement when in reality it doesn’t.

It’s often the case with false positives that version B gives the same results as version A (not that it performs inadequately compared to version A).

While by no means innocuous, false positives certainly aren’t a reason to abandon A/B testing. Instead, you can adjust your confidence interval to fit the risk associated with a potential false positive.

Gain probability using Bayesian statistics

You’ve probably heard of the 95% gain probability rule of thumb.

In other words, you consider that your test is statistically significant when you’ve reached a 95% certainty level. You’re 95% sure your version B is performing as indicated, but there’s still a 5% risk that it isn’t.

For many marketing campaigns, this 95% threshold is probably sufficient. But if you’re running a particularly important campaign with a lot at stake, you can adjust your gain probability threshold to be even more exact – 97%, 98% or even 99%, practically ruling out the potential for a false positive.

While this seems like a safe bet – and it is the right choice for high-stakes campaigns – it’s not something to apply across the board.

This is because:

In order to attain this higher threshold, you’ll have to wait longer for results, therefore leaving you less time to reap the rewards of a positive outcome.
You will implicitly only get a winner with a bigger gain (which is rarer), and you will let go of smaller improvements that still could be impactful.
If you have a smaller amount of traffic on your web page, you may want to consider a different approach

Bayesian tests limit false positives

Another thing to keep in mind is that because the Bayesian approach provides a gain interval – and because false positives virtually only appear to perform slightly better than in reality – you’re unlikely to implement a false positive in the first place.

A common scenario would be that you run an A/B test to test whether a new promotional banner design increases CTA click-through rates.

Your result says version B performs better with a 95% gain probability but that the gain is minuscule (1% median improvement). Were this to be a false positive, you’re unlikely to deploy the version B promotional banner across your website, since the resources needed to implement it wouldn’t make it worth the minimum again.

But, since a Frequentist approach doesn’t provide the gain interval, you might be more tempted to put in place the false positive. While this wouldn’t be the end of the world – version B likely performs the same as version A – you would be spending time and energy on a modification that won’t bring you any added return.

Bottom line? If you play it too safe and wait for a confidence level that’s too high, you’ll miss out on a series of smaller gains, which is also a mistake.

Wrapping up: Frequentist vs Bayesian

So, which is better, Frequentist or Bayesian?

As we mentioned early, both approaches are perfectly sound, statistical methods.

But at AB Tasty, we’ve opted for the Bayesian approach, since we think it helps our clients make even better business decisions on their web experiments.

It also allows for more flexibility and maximizing returns (Dynamic Traffic Allocation). As for false positives, these can occur whether you go with a Frequentist or Bayesian approach – though you’re less likely to fall for one with the Bayesian approach.

At the end of the day, if you’re shopping for an A/B testing platform, you’ll want to find one that gives you easily interpretable results that you can rely on.

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The Truth Behind the 14-Day A/B Test Period

Hubert Wassner

The A/B testing method involves a simple process: create two variations, expose them to your customer, collect data, and analyze the results with a statistical formula.

But, how long should you wait before collecting data? With 14 days being standard practice, let’s find out why as well as any exceptions to this rule.

Why 14 days?

To answer this question we need to understand what we are fundamentally doing. We are collecting current data within a short window, in order to forecast what could happen in the future during a more extended period. To simplify this article, we will only focus on explaining the rules that relate to this principle. Other rules do exist, which mostly correlate to the number of visitors, but this can be addressed in a future article.

The forecasting strategy relies on the collected data containing samples of all event types that may be encountered in the future. This is impossible to fulfill in practice, as periods like Christmas or Black Friday are exceptional events relative to the rest of the year. So let’s focus on the most common period and set aside these special events that merit their own testing strategies.

If the future we are considering relates to “normal” times, our constraint is to sample each day of the week uniformly, since people do not behave the same on different days. Simply look at how your mood and needs shift between weekdays and weekends. This is why a data sampling period must include entire weeks, to account for fluctuations between the days of the week. Likewise, if you sample eight days for example, one day of the week will have a doubled impact, which doesn’t realistically represent the future either.

This partially explains the two-week sampling rule, but why not a longer or shorter period? Since one week covers all the days of the week, why isn’t it enough? To understand, let’s dig a little deeper into the nature of conversion data, which has two dimensions: visits and conversions.

Visits: as soon as an experiment is live, every new visitor increments the number of visits.
Conversions: as soon as an experiment is live, every new conversion increments the number of conversions.

It sounds pretty straightforward, but there is a twist: statistical formulas work with the concept of success and failure. The definition is quite easy at first:

Success: the number of visitors that did convert.
Failures: the number of visitors that didn’t convert.

At any given time a visitor may be counted as a failure, but this could change a few days later if they convert, or the visit may remain a failure if the conversion didn’t occur.

So consider these two opposing scenarios:

A visitor begins his buying journey before the experiment starts. During the first days of the experiment he comes back and converts. This would be counted as a “success”, but in fact he may not have had time to be impacted by the variation because the buying decision was made before he saw it. The problem is that we are potentially counting a false success: a conversion that could have happened without the variation.
A visitor begins his buying journey during the experiment, so he sees the variation from the beginning, but doesn’t make a final decision before the end of the experiment – finally converting after it finishes. We missed this conversion from a visitor who saw the variation and was potentially influenced by it.

These two scenarios may cancel each other out since they have opposite results, but that is only true if the sample period exceeds the usual buying journey time. Consider a naturally long conversion journey, like buying a house, measured within a very short experiment period of one week. Clearly, no visitors beginning the buying journey during the experiment period would have time to convert. The conversion rates of these visitors would be artificially in the realm of zero – no proper measurements could be done in this context. In fact, the only conversions you would see are the ones from visitors that began their journey before the variation even existed. Therefore, the experiment would not be measuring the impact of the variation.

The delay between the effective variation and the conversion expedites the conversion rate. In order to mitigate this problem, the experiment period has to be twice as long as the standard conversion journey. Doing so ensures that visitors entering the experiment during the first half will have time to convert. You can expect that people who began their journey before the experiment and people entering during the second half of the experiment period will cancel each other out: The first group will contain conversions that should not be counted, and some of the second group’s conversions will be missing. However, a majority of genuine conversions will be counted.

That’s why a typical buying journey of one week results in a two-week experiment, offering the right balance in terms of speed and accuracy of the measurements.

Exceptions to this rule

A 14-day experiment period doesn’t apply to all cases. If the delay between the exposed variation and the conversion is 1.5 weeks for instance, then your experiment period should be three weeks, in order to cover the usual conversion delay twice.

On the other hand, if you know that the delay is close to zero, such as in the case of a media website, where you are trying to optimize the placement of an advertisement frame on a page where visitors only stay a few minutes, you may think that one day would be enough based on the this logic, but it’s not.

The reason being that you would not sample every day of the week, and we know from experience that people do not behave the same way throughout the week. So even in a zero-delay context, you still need to conduct the experiment for an entire week.

Takeaways:

Your test period should mirror the conditions of your expected implementation period.
Sample each day of the week in the same way.
Wait an integer number of weeks before closing an A/B test.

Respecting these rules will ensure that you’ll have clean measures. The accuracy of the measure is defined by another parameter of the experiment: the total number of visitors. We’ll address this topic in another article – stay tuned.

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Your Guide to Product Listing Pages and How to Optimize Them (+ Examples)

Emily Healy

If you ask most e-commerce marketers how to optimize your website to generate more conversions, they’ll tell you to focus on your homepage or product detail pages. While that answer is technically correct, there is a potential goldmine for clicks that even the most seasoned marketers overlook: product listing pages

While these pages are often used as a catalog for your products and services, they can offer much more than an opportunity to optimize the customer experience. Since visitors browsing your product listing pages are already engaged with your online store, they just need one final push to convert.

In this article, we’ll show you everything you need to know about product listing pages, how to optimize your PLPs, and some examples of great product listing pages.

What are product listing pages?

Product listing pages (sometimes called PLPs or category landing pages) are pages on a website that display products based on a selected category; they may also be based on applied search filters. Product landing pages lead visitors to product detail pages where they can find more information on the items they’re interested in or even add said items to their cart.

One of the main drivers for optimizing your product listing pages is the opportunity they present for optimizing your user experience, as they can be tailored to shoppers with different user intent. On the one hand, we have buyers who visit a website knowing exactly what they are looking for. These potential buyers want to view the items that are most relevant to their search or intent (e.g. a user looking for a mountain bike doesn’t want to view road bikes). On the other hand, other visitors simply love browsing and use your PLPs to sift through the list of products that suit their preferences.

Of course, your product listing pages are not only about navigation and UX. They do much more. With a well-optimized PLP, you’ll be able to boost your SEO, increase user engagement, and improve your conversion rate!

Key elements of a product listing page and how to design your PLPs for better conversions

Creating an effective product listing page starts with the basics. Designing your product listing pages in an optimal way, with all the relevant elements, will increase the odds of shoppers finding the products they are looking for and making a purchase. Here’s what you should make sure to include in your product listing pages:

1. PLP page name: Descriptive title

Remember that Google will only display the first 25, 50, or 64 characters of your title, so make sure your PLP title is optimized accordingly. For example, if you are selling cell phones, you might want to structure your titles according to make, model, memory size, and color so that shoppers can see the most important information upfront.

2. Description: Keyword-rich

The product description and title have a big impact on your PLP’s SEO and product discoverability. Make sure that your descriptions are thorough and contain all the relevant keywords that will help you rank higher. Remember: the more specific, the better.

3. Breadcrumbs: Proper category name

Make sure that each product is placed in the most relevant category to both orient your shoppers and help them discover similar products. Breadcrumbs can display the parent category/subcategories so that users can jump back and forth between product listing pages with ease.

4. Imagery: Thumbnail

People process visual information faster than anything else, and your product images will be the first thing a customer sees. Use high-quality photos and be consistent (for example, use the same color background for every image). If you use various backgrounds, colors, and sizes, your customers will be distracted. Want proof? Read our case study on Hanna Andersson where they have proven that keeping all images simple, clear, and harmonized will work wonders for results.

5. Price

Make sure that your pricing is competitive. Do your research and benchmark your prices against your competitors and make amendments. Highlight any other elements that make your pricing more competitive, like free shipping, or buy-one-get-one-free offers.

6. CTAs

Call to action buttons (also known as CTAs) are items that use imperative wording to nudge your users towards the action you want them to take, like “Add to cart now!” or “Save to Wishlist” if a product is unavailable. It’s important to create an effective CTA by following design best practices and carefully testing different variations of your call to action’s copy, location and colors.

Make sure that your button is visible against the background and all the other elements on the page. This not only draws the visitor’s eye to the call to action but shows them that the button is clickable. It’s important that your button looks like a button, even if you want to adopt a more minimalistic design for your website.

Next, make sure that your call to action conveys urgency. Using phrases like “Sign up now,” “Hurry” or “Don’t delay” encourages your users to not only act, but to act fast. It’s also a good idea to utilize first-person copy so that the visitor feels more connected to the CTA.

7. Filter menu

This menu displays the filtering options available to refine searches by attributes, like pricing, color, style, availability, size, and more. This will help your customers find what they are looking for easily.

8. Sorting menu

The sorting menu presents different options for organizing products using a dropdown menu, including “Price: Low to High,” “Newest,” or “Rating: High to Low.”

Sorting options have a sole purpose: narrowing down the number of products in order to increase conversion. Your sorting options should be based on your audience’s needs and expectations regarding your products. Thinking in the minds of your customers is crucial for optimization at this point in the digital customer journey.

Let’s take WatchShop as an example.

How to sort products in e-commerce listing page

WatchShop knows that watches come in all sorts of shapes and colors, so they created various sorting options to match visitors’ requests. This includes water resistance, strap type, case color, movement type, and so on.

It’s all tailored to match customers’ expectations – and it delivers.

10 best practices for creating and optimizing product listing pages

Now that you know how to design your product listing pages, let’s get into how to optimize them for the best results:

1. Optimize headers or banners

Headers play the “title” role of each category and listing page design.

They’re the main indication of the page’s content and should be treated as the most important thing. If the header does not properly describe the page or the category, visitors will not be able to find what they are looking for.

Headers can reinforce your branding, so use the space on the top of your page to create a great-looking banner that engages and informs without adding clutter. In addition, never forget to include your keywords inside your <h1> tags. Not only will this make them more visible on the page, but they’re also a bit part of your on-page SEO efforts that will help you appear higher in search results.

In the image below, beauty specialist Ulta bets on shiny visuals to increase its headers’ visibility. It’s a good solution to avoid “all text” headers that can seem dull at first glance.

Take care of the headers of your product page

Note: headers can also be used as promotional spaces to display featured products, special offers, and discounts.

2. Experiment with multiple layouts on your product listing display

Your product listing display has a significant impact on the way your customers interact with the products. Unfortunately, there’s no easy answer when it comes to choosing between list and grid views. In fact, it all depends on what type of products you’re selling and what experience you want to provide. The most common choices are list view or grid view:

List view

List views require a little more scrolling but can display more product information than a grid. This makes it easy for shoppers to compare product attributes, like dimensions or features. Some sites let customers toggle between a list or grid view, depending on their preferences.

The list view is better suited for products that require extensive information and specifications in order to help customers compare aspects of similar products.

It’s a great fit for technical products like TVs, computers, electronics, DVDs, hardware, etc. However, this isn’t the only time to use list view for your product pages.

If we take a look at the image below, Wine.com sells luxury wines and champagnes. In this case, it’s important that visitors take their time benchmarking the brands and “grands crus” before making a purchase decision.

Notice how they capitalize on the extra horizontal space to display ratings.

Pros of list view for listing product page

Grid view

Grid views allow customers to browse and compare products next to each other. This is a good option if your site is picture-heavy and doesn’t require a lot of description outside the product titles.

Grid view is mostly used for products that rely a lot on pictures and can be compared quickly without paying much attention to the specifications. It also allows for more visual experiences.

Amazon uses grid view to display products inside its “gift ideas” category. They also use tags to rank the bestsellers and lure visitors into clicking on the products based on their popularity.

Grid view pros for listing page

3. Add persuasion triggers

Persuasion triggers create a sense of urgency or scarcity. You most often see this on hotel booking sites or the sales section of a fashion store (“Only 1 item left at this price!” or “Selling out fast!”). These labels trigger visitors’ fear of missing out and push them to take action, so be sure to add them to your images to nudge them into making a purchase.

4. Personalize the shopping experience

Personalization can dramatically increase conversions, boost engagement, and help shoppers discover relevant products by sorting them according to their individual preferences. Personalization has also been known to reduce bounce rates by 20-30% and increase customer loyalty.

One thing you can do to personalize the experience for your visitors is to display complementary products that they might be interested in. For example, customers shopping for a new bedspread might also be interested in buying pillowcases or sheets, so steer them in that direction.

5. Use recommendations

If someone is already browsing your product listing page, the chances are that they already have the intent of making a purchase. This is the best time to make suggestions and cross-sell or upsell your products:

Some customers suffer from decision fatigue when they are presented with too many options. Gently recommend popular products, others within the same category and with the same tags, or similar (but slightly pricier) alternatives.
Show customers recommended products that might be relevant to the one they are viewing. For example, clients who are interested in a technical product would appreciate a “People who purchased this product also purchased” section that shows the accessories that go along with it.
Present seasonal bestsellers to add specificity and relevance, which could lead to more clicks and conversions. We tend to think that other people’s actions are the correct ones, so if a product is tagged as “trending,” it gains additional legitimacy that could push a customer to make a decision.

Want more information about buyer behaviors in e-commerce? Check out our 2024 consumer study report!

6. Implement intuitive navigation

Your navigation has to be tailored to help prospective customers find what they are looking for as easily as possible with as little friction as possible. There are a few tips and tricks you can try, including:

Put your best-selling items front and center: We’ve already touched on the fact that customers like knowing what items others are buying. The most popular options are often seen as the safest ones to buy. Throw in some social proof messages like user ratings to really drive the point home.
Site speed is a crucial factor for UX: Make sure that your site loads quickly on both desktop and mobile devices to ensure that customers have an enjoyable experience.
Ensure that your navigation bar is fixed to the top of the page and organizes your products in a logical fashion.

No matter the level of page depth, navigation always plays a crucial role in the user’s experience – and your product listing page should not be different. Because some products have complex specifications and require extensive sorting options, pay attention to your website’s performance when it comes to sorting products and helping customers find their perfect product.

In the image below, RevZilla does a great job of guiding customers through the endless journey of finding the right motorcycle helmet.

They use their left column to help customers sort and rank products according to several criteria (faceted search):

Color
Type
Shape
Category
Size
Gender
Bonus point: RevZilla provides visitors the opportunity to only display products that have a video review. This is a huge value proposition compared to their competitors.

7. What information to display on your PLPs?

There are tons of options regarding which information you can display on your product listing pages and category pages. Simply put, you need to display information that will effectively help and convince consumers to move down the funnel and make a purchase.

In order to help you choose, here’s a list of information that may be displayed on your product listing page:

Star ratings
Discounts
Color options
Stock availability
Best-sellers
Add to cart
New / Used
Short descriptions

As an example in the image below, BestBuy does a great job of providing useful information on its product listing page. Besides the pictures and the price, they also added: star ratings, discounts, and an add to cart button with a smart color hierarchy.ㅤ What information to display on a product listing page

8. Optimize SEO for product listing pages

SEO is a big deal for most e-commerce players. In fact, search engine traffic accounts for around 50% of all e-commerce traffic according to a 2023 study led by SmartInsights.

There are two main reasons that justify the dominance of product listing pages regarding SEO:

A. Product listing pages are keyword-rich

Because they contain the names, brands, prices, specifications, and descriptions of products, category pages tend to be keyword-rich. This means that they naturally rank for a lot of keywords in search engines.

B. Product listing pages are the most heavily linked to

Product listing pages are typically where you want your customers to start their journey (or alternatively on the product page itself), which is why SEO pros tend to focus their efforts on these pages. Besides this, all products within a category generally link back to that category, which is a strong internal link-building pattern.

Tips for optimizing SEO on your product listing pages:

Optimizing your title tags
Using unique and original product and meta descriptions
Linking to internal pages
Using image alt attributes and rich snippets

9. Should you use Quick View or add-to-cart buttons?

Quick View is an e-commerce function that allows visitors to generate a miniature version of the desired product page. In other words, it’s a mini product page that generally embeds a direct “add to cart” button.

Not all products require lengthy deliberation and consideration before making a purchase, especially for returning customers or others purchasing fast-moving goods like groceries. Creating add-to-cart buttons makes it easier to speed through the checkout process. You can also implement add-to-wishlist buttons for more complex or expensive items to maximize conversions.ㅤ

10. Use clear and concise CTAs

Call to action buttons can have a massive impact on your conversions. When Dutch watch brand Cluse noticed that their product listing pages had high bounce rates (and that clicks to the product display pages were low), they turned to AB Tasty to find a solution.

Cluse set up a simple test to see whether changing their CTA’s location and color would improve the results. The team’s hypothesis was correct, and the site saw a 2.39% increase in the click-through rate to the product display page and a 1.12% uplift in transactions during the three-week test.

CLUSE User generated content

Examples of effective product listing pages

ASOS

ASOS uses short but descriptive copy on their product listing pages.

ASOS store image

The clothing retailer’s product listing pages are categorized by trends and style. They use extremely simple copy and appealing photographs to convince shoppers to make a purchase. Users can add items to their wishlist or cart directly from the product listing page and check out using the simple navigation banner.

Everlane

Everlane uses quick add-to-cart buttons to optimize their product listing pages

Everlane page image

Everlane uses a number of features from the best practice guidebook, including adding product size options as an overlay in the image, easy navigation using the grid view and sidebar, and quick add-to-cart buttons.

Walmart

Walmart uses compelling headers and content on their product listing page.

Walmart page image

Walmart puts bestsellers on top of their product listing pages, Walmart puts bestsellers on top of their product listing pages, along with engaging headers that feature collections by influencers like Sofia Vergara and Kim Kardashian. They also use quick add-to-cart buttons to make it easier to shop. The copy is clear and concise, and users are able to comfortably scroll through galleries of attractive images. Returning users are greeted with a warm, personalized message.

How many products per page and per row to display on your PLP?

If you opt for a grid view template, there’s no doubt that you’ll eventually come to the question, how many products per row?

As for choosing between list view and grid view, there’s no single answer that will fit everyone’s needs. In fact, the number of products displayed per row depends on 3 main factors:

Image size – If you choose to display big, high-resolution images; there’s no doubt that you will have a hard time squeezing more than 4-5 products in a row.
Number of products – The number of products listed per row also depends on your total number of products for a given category. If you only have 12 products to display, it’s a lot more coherent to opt for a 4X3 grid structure rather than 2X6. You need to fill the page visually.
Volume of information – Not all products are considered equal when it comes to their product description. Some products natively require more information than others. The more space they need, the fewer products you will display.

In the image below, Canada Goose, a high-quality outerwear provider, relies on a 2-products-per-row structure. This strategy highlights the visuals and delivers a more premium feeling to the user’s experience.

Ideal number of products to display on a e-commerce listing page

How to find what works best on your product listing pages?

A/B test your product listing pages.

There is no secret when it comes to Conversion Rate Optimization (CRO) – testing is what makes it work. The recipe for success doesn’t change for your product listing pages, you just have to A/B test them.

Now the question is, how can you do that? We have great news for you: we’re A/B testing specialists.

Making a good product listing page isn’t easy. You will have to identify elements that work and elements that don’t to gradually increase your conversions and offer an overall better user experience to your customers.

Want to start optimizing your product listing pages? AB Tasty is the best-in-class experience optimization platform that empowers you to create a richer digital experience – fast. From experimentation to personalization to smart search and recommendations, our solution can help you activate and engage your audience to boost your conversions.

Conclusion: The ultimate product listing page

Product listing pages can be conversion machines. When they’re properly optimized, they’re key for delivering an exceptional customer experience, helping you rise to the top of search engine results, and increasing basket size.

Whether you’re a seasoned seller or are venturing into the world of e-commerce for the first time, it may seem overwhelming to hit all the right notes – and find the best ideas to take your product listing pages to the next level!

Creating product listing pages will look a bit different depending on your market sector. However, for maximum performance, keep these best practices in mind for your e-commerce brand.

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